In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

AI

This course was a good intro especially in setting all the necessary software for future courses. I suggest to read the manuals, books and other readings the profs suggest. The resources are helpful.

WC

Sep 26, 2017

Filled StarFilled StarFilled StarFilled StarFilled Star

I really don't know much about this stuff, I think the jury's still out on whether the last four weeks will be helpful in the future. We'll see how much I think I've learned at the end of the course

수업에서

Week 1

During Week 1, you'll learn about the goals and objectives of the Data Science Specialization and each of its components. You'll also get an overview of the field as well as instructions on how to install R.

강사:

Jeff Leek, PhD

Associate Professor, Biostatistics

Roger D. Peng, PhD

Associate Professor, Biostatistics

Brian Caffo, PhD

Professor, Biostatistics

스크립트

This is a brief follow-up video to getting help and it's about finding answers. The reason why there are two videos about this is because it's such a critical skill in data science. So you can see one of the three fundamental sort of, skills you see in the Venn diagram here is Hacking Skills. And the reason why it's one of the three fundamental skill is because almost of none of the knowledge that you will need is already sort of set in standardized text books. It's often scattered in a bunch of different places and you have to be able to sort of synthesize it or find the information that you need about. Whether it's about which data set you need to be using, or the statistical analysis you need to be doing, or the R Package that you need to be using. All of this is sort of scattered around, and you have to be willing to do a little bit of hard work and elbow grease to find it yourself. Obviously we'll tell you as much as we can in lectures, but we're necessarily limited by the amount of time that we can lecture every week, and so it's important to be able to find that information yourself. So key, some key characteristics packers are that they're willing to go out and find the answers on their own even if it takes a little bit of time or a little bit of effort. They're knowledgeable about where to find those answers whether its Google, or stack over glow, or cat. Cross validated or a message fo history of mailing lists. They're unintimidated by new data types or packages. It's very common as a data scientist to be thrown a new kind of data or a new kind of Our package that you need to learn very quickly to be able to analyze the data to being unintimidated by that is important and then being unafraid to say that you don't know the answer to a question. And so a key characteristic I would say the way to summarize it up is being alive but relentless here. Going after the answer and you just trying to find it But you're very polite while doing it. And so Google knows this too. In their hiring practices they're looking for these sort of characteristics. The kind of people that will go after these sorts of things as it's described in this article, I'm linked to here. [SOUND] So an important question is where to look for, for different types of questions. So for our programming you might want to go straight to the the archive of the class forums. Where the class you're taking will focus on very specific questions or functions. And there'll be a large group of interested people. You could read the manual or help files like I showed you in the getting help lecture. You can search on the web. That's actually one of the best ways to do it. You can ask a skilled friend. That's even better if you've got a person that you know that already is a bit of a data scientist. They can often help you out. And then you can post to the class forums and try to get your answers. Remember to be specific with your questions. You can also post to forums outside of the class. The R Mailing List or Stackoverflow, if you have R questions. For data analysis or statistics type questions, you want to go to start again with the class forums, and then go to the web or to friends. And then there's another outside forum called CrossValidated where you can ask these types of questions. Brother software you might have to go to software specific websites. So forget HUB, they have a lot of tutorials and nice information that you can use to get answers there. [SOUND] So an important point to know is that Googling data science questions isn't always the easiest thing in the world. So, the best place to start with if you have a pretty general question is often in the forums. And if people can direct you to where you should be searching outside of the forums. Keep in mind that Stackoverflow with the tag R is a really good place to get information about R. And, so, you have to use this tag because if you just use the letter R, it obviously is, sometimes, a little bit hard to search for. You can also try the R mailing list for software questions or CrossValidated for more general questions. Usually what I've found is that if I'm going to work in Google, searching Google, I use usually type something like the data type and then data analysis or I type the data type and then the R package. I found that data type R package often works a little bit better than data type, data analysis when Googling things. Another thing to keep in mind is that data analysis or data science is often called something different depending on what kind of data you are looking at, so for example medical data it might be called biostatistics. For data from the web it might be data science. For data in computer vision it might be machine learning or natural language processing for data from text and so on. So, knowing what the right word to Google is, is often half the battle. And so, you can often find that out by posting to forums and people will let you know what the right word to be googling is. [SOUND] Again the credits for this go to Roger's Getting Help Video. And it was inspired by Eric Raymond's How to Ask Questions the Right Way.